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Managing Lookup Tables and Enrichment Providers with the Panther Analysis Tool

Manage Custom Lookup Tables and Enrichment Providers using PAT

Overview

You can manage Custom Lookup Table and Enrichment Provider (also known as Panther-managed Lookup Table) schemas and mappings through the Panther Analysis Tool (PAT).

This guide will walk you through the following:

  • Creating and uploading a custom schema for a Custom Lookup Table using the pantherlog tool.

  • Modifying the Selectors and LogTypes in the Lookup Table/Enrichment Provider YAML configuration file.

  • Uploading the Lookup Table/Enrichment Provider YAML configuration file via PAT.

  • Testing the enrichment in the Panther Console.

  • If your team uses CLI workflows, it's recommended to use PAT and CI/CD to manage your Enrichment, instead of doing so via Detection Packs in the Console.

  • If you choose to manage Lookup Tables through PAT after enabling them in the Panther Console, you must first disable the Detection Packs in the Panther Console. Simultaneous use of both the Panther Console and PAT to manage Lookup Tables is not supported.

This guide applies to all Enrichment Providers except for Anomali ThreatStream, which cannot be enabled in the CLI workflow using PAT.

Custom Lookup Tables vs. Enrichment Providers

In Panther, there is a distinction between Custom Lookup Tables and Enrichment Providers (also known as Panther-managed Lookup Tables):

  • Custom Lookup Tables are user-managed. You'll need to create and upload a schema, then upload the Lookup Table's YAML configuration file.

  • Enrichment Providers are Panther-managed Lookup Tables. Their schemas are Panther-defined, and their YAML configuration files (which you can modify to your needs) can be found in the panther-analysis repo in GitHub.

How to manage Custom Lookup Tables and Enrichment Providers with PAT

Prerequisites

  • A YAML configuration file. You must create the YAML configuration file yourself.

  • A data sample (if you need to create a new schema) or an existing YAML schema created in Panther.

Step 1: Create and upload a schema

Custom Lookup Tables must be associated with a schema you have created and uploaded to Panther. If you have already created a schema in Panther that you want associate to your Lookup Table, you can skip this step.

  1. Create the schema using your sample log data.

    • You can use pantherlog to infer a schema from a sample set of data. To generate a schema from a sample JSON log file, use the infer command:

      $ ./pantherlog infer sample_logs.jsonl > schema.yml

    • Remember to review the inferred schema and make any necessary adjustments before uploading it to Panther. For more information about this process, see the pantherlog documentation.

  2. Upload the schema.

Step 2: Create the YAML configuration file

Step 3: Upload the Lookup Table via PAT

Once you have created your custom Lookup Table configuration file, you can upload it to Panther using the Panther Analysis Tool's upload command:

panther_analysis_tool upload

You will need to provide an API token and host with --api-token and --api-host, respectively, for the upload to occur. Other options include filtering, minimum tests, and more.

Ensure you've uploaded the corresponding schema before uploading the YAML configuration file.

Step 4: Test the Lookup Table

There are several methods to test if your Lookup Table has been set up correctly.

Method 1: Enriching test data in the Panther Console or CLI

In the Panther Console's detection editor, click Enrich Test Data to verify if your Lookup Table is working correctly. This allows you to input test data and see the output of the enrichment process within your unit test.

For Enrich Test Data to work, the unit test must have a p_log_type identifying the correct log type. This serves as the basis for Panther's enrichment logic.

Method 2: Checking the panther_rule_matches database

You can verify that your changes have taken effect by checking the panther_rule_matches database for the p_enrichment field. Ensure that the field includes the Lookup Table details you would expect to see.

Method 3: Using SQL queries

You can also perform a LEFT JOIN between event logs and the lookup table in SQL. Ensure that the selector is defined in the query. This allows you to verify if the data from your logs is being correctly matched with the data in your Lookup Table.

For example, this query will attempt to match event data to the Lookup Table using a custom selector (which should be the same as the selector you've defined in the YAML configuration file):

SELECT *
FROM panther_logs.public.<log_type> AS e
LEFT JOIN panther_lookups.public.<lookup_table_name> AS lt
ON e.<field_path> = lt.<field_path>
WHERE e.p_occurs_since('1 day')
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